1. Field of the Invention
The present invention relates to visual information retrieval systems. More specifically, the invention is directed to an extensible system for retrieval of stored visual objects based on similarity of content to a target visual object.
2. Description of the Related Technology
One of the most important technologies needed across many traditional and emerging applications is the management of visual information. Every day we are bombarded with information presented in the form of images. So important are images in our world of information technology, that we generate literally millions of images every day, and this number keeps escalating with advances in imaging, visualization, video, and computing technologies.
It would be impossible to cope with this explosion of image information, unless the images were organized for rapid retrieval on demand. A similar situation occurred in the past for numeric and other structured data, and led to the creation of computerized database management systems. In these systems, large amounts of data are organized into fields and important or key fields are used to index the databases making search very efficient. These information management systems have changed several aspects of the modern society. These systems, however, are limited by the fact that they work well only with numeric data and short alpha-numeric strings. Since so much information is in non-alphanumeric form (such as images, video, speech), to deal with such information, researchers started exploring the design and implementation of visual databases. But creation of mere image repositories is of little value unless there are methods for fast retrieval of objects such as images based on their content, ideally with an efficiency that we find in today's databases. One should be able to search visual databases with visual-based queries, in addition to alphanumeric queries. The fundamental problem is that images, video and other similar data differ from numeric data and text in format, and hence they require a totally different technique of organization, indexing, and query processing. One needs to consider the issues in visual information management, rather than simply extending the existing database technology to deal with images. One must treat images as one of the central sources of information rather than as an appendix to the main database.
A few researchers have addressed problems in visual databases. Most of these efforts in visual databases, however, focussed either on only a small aspect of the problem, such as data structures or pictorial queries, or on a very narrow application, such as databases for pottery articles of a particular tribe. Other researchers have developed image processing shells which use several images. Clearly, visual information management systems encompass not only databases, but aspects of image processing and image understanding, very sophisticated interfaces, knowledge-based systems, compression and decompression of images. Moreover, memory management and organization issues start becoming much more serious than in the largest alphanumeric databases.
A significant event in the world of information systems in the past few years is the development of multimedia information systems. A multimedia information system goes beyond traditional database systems to incorporate various modes of non-textual digital data, such as digitized images and videos, in addition to textual information. It allows a user the same (or better) ease of use and flexibility of storage and access as traditional database systems. Today, thanks to an ever-increasing number of application areas like stock photography, medical imaging, digital video production, document imaging and so forth, gigabytes of image and video information are being produced every day. The need to handle this information has resulted in new technological requirements and challenges:
Image and video data are much more voluminous than text, and need supporting technology for rapid and efficient storage and retrieval. PA1 There are several different modes in which a user would search for, view, and use images and videos. PA1 Even if multimedia information resides on different computers or locations, it should easily be available to the user. PA1 Query by image property, wherein a user specifies a property or attribute of the image, such as the arrangement of colors, or they may sketch an object and request the system to find images that contain similar properties. The Engine also allows the user to specify whether or not the location of the property in the image (e.g., blue at the bottom of the image or blue anywhere) is significant. PA1 Query by image similarity, wherein a user provides an entire image as a query target and the system finds images that are visually similar. PA1 Query refinement or systematic browsing. With any of the previous modes of query, the system produces some initial results. A browsing query is one that refines the query by either choosing an image from the previous result set, or by modifying the parameters of the original query in some way. The system in this situation reuses the previous results to generate refined results.
Thus, representation, storage, retrieval, visualization and distribution of multimedia information is now a central theme both in the academic community and industry alike. What is needed is a capability to manage this information. In traditional database systems, users search images by keywords or descriptions associated with the visual information. In a traditional database management system (DBMS), an image is treated as a file name, or the raw image data exists as a binary large object (BLOB). The limitation is clear: a file name or the raw image data is useful for displaying the image, but not for describing it. In some applications, these shortcomings were overcome by having a person participate in the process by interpreting and assigning keyword descriptions to images. However, textual descriptors such as a set of keywords are also inadequate to describe an image, simply because the same image might be described in different ways by different people. What is needed is a new multimedia information system technology model such as a visual information management system (VIMSYS) model. Unlike traditional database systems, this model recognizes that most users prefer to search image and video information by what the image or video actually contains, rather than by keywords or descriptions associated with the visual information. The only proper method by which the user can get access to the content of the image is by using image-analysis technology to extract the content from an image or video. Once extracted, the content represents most of what the user needs in order to organize, search, and locate necessary visual information.
This breakthrough concept of content extraction alleviates several technological problems. The foremost benefit is that it gives a user the power to retrieve visual information by asking a query like "Give me all pictures that look like this." The system satisfies the query by comparing the content of the query picture with that of all target pictures in the database. This is called Query By Pictorial Example (QBPE), and is a simple form of content-based retrieval, a new paradigm in database management systems.
Over the last five years research and development in content-based retrieval of visual information has made significant progress. Academic research groups have developed techniques by which images and videos can be searched based on their color, texture, shape and motion characteristics. Commercial systems supporting this technology, such as Ultimedia Manager from IBM, and the Visual Intelligence Blade from Illustra Information Technologies, Inc. are beginning to emerge.
A typical content-based retrieval system might be described as follows: image features are precomputed during an image insertion phase. These representations may include characteristics such as local intensity histograms, edge histograms, region-based moments, spectral characteristics, and so forth. These features are then stored in a database as structured data. A typical query involves finding the images which are "visually similar" to a given candidate image. In order to submit a query, a user presents (or constructs) a candidate image. This query image may already have features associated with it (i.e., an image which already exists within the database), or may be novel, in which case a characterization is performed "on the fly" to generate features. Once the query image has been characterized, the query executes by comparing the features of the candidate image against those of other images in the database. The result of each comparison is a scalar score which indicates the degree of similarity. This score is then used to rank order the results of the query. This process can be extremely fast because image features are pre-computed during the insertion phase, and distance functions have been designed to be extremely efficient at query time. There are many variants on this general scheme, such as allowing the user to express queries directly at the feature level, combining images to form queries, querying over regions of interest, and so forth.
General systems (using color, shape, etc.) are adequate for applications with a broad image domain, such as generic stock photography. In general, however, these systems are not applicable to specific, constrained domains. It is not expected, for example, that a texture similarity measure that works well for nature photography will work equally well for mammography. If mammogram databases need to be searched by image content, one would need to develop specific features and similarity measures. This implies that a viable content-based image retrieval system will have to provide a mechanism to define arbitrary image domains and allow a user to query on a user-defined schema of image features and similarity metrics.
There is a need to provide a way to compare images represented by different schemas. There is also a need to reduce the time performing the comparison, especially when large numbers of images are in the database.